Overview

Dataset statistics

Number of variables13
Number of observations3040
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory297.0 KiB
Average record size in memory100.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 5 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with customer_idHigh correlation
qtde_returns is highly correlated with gross_revenue and 3 other fieldsHigh correlation
customer_id is highly correlated with avg_unique_basket_sizeHigh correlation
avg_ticket is highly skewed (γ1 = 39.09010284) Skewed
qtde_returns is highly skewed (γ1 = 38.01023083) Skewed
avg_basket_size is highly skewed (γ1 = 40.99969797) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 90 (3.0%) zeros Zeros
avg_recency_days has 81 (2.7%) zeros Zeros
qtde_returns has 1535 (50.5%) zeros Zeros

Reproduction

Analysis started2022-09-27 11:35:53.146746
Analysis finished2022-09-27 11:37:56.375025
Duration2 minutes and 3.23 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct3040
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2327.782566
Minimum0
Maximum5677
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:37:56.899896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile190.95
Q1956.5
median2134.5
Q33537
95-th percentile5058.05
Maximum5677
Range5677
Interquartile range (IQR)2580.5

Descriptive statistics

Standard deviation1546.466985
Coefficient of variation (CV)0.6643519922
Kurtosis-1.021504017
Mean2327.782566
Median Absolute Deviation (MAD)1274
Skewness0.3235780558
Sum7076459
Variance2391560.135
MonotonicityStrictly increasing
2022-09-27T08:37:57.680447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30351
 
< 0.1%
30221
 
< 0.1%
30231
 
< 0.1%
30241
 
< 0.1%
30281
 
< 0.1%
30291
 
< 0.1%
30311
 
< 0.1%
30321
 
< 0.1%
30331
 
< 0.1%
Other values (3030)3030
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56771
< 0.1%
56751
< 0.1%
56241
< 0.1%
56071
< 0.1%
55971
< 0.1%
55921
< 0.1%
55911
< 0.1%
55851
< 0.1%
55701
< 0.1%
55681
< 0.1%

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3040
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15306.15099
Minimum12346
Maximum22269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2022-09-27T08:37:58.301867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12620.95
Q113815.75
median15255
Q316804.25
95-th percentile18008.05
Maximum22269
Range9923
Interquartile range (IQR)2988.5

Descriptive statistics

Standard deviation1744.410149
Coefficient of variation (CV)0.1139679173
Kurtosis-1.015418269
Mean15306.15099
Median Absolute Deviation (MAD)1493.5
Skewness0.08263686169
Sum46530699
Variance3042966.769
MonotonicityNot monotonic
2022-09-27T08:37:58.863258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
151281
 
< 0.1%
132821
 
< 0.1%
155821
 
< 0.1%
162301
 
< 0.1%
181461
 
< 0.1%
159761
 
< 0.1%
128861
 
< 0.1%
179741
 
< 0.1%
135241
 
< 0.1%
Other values (3030)3030
99.7%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
ValueCountFrequency (%)
222691
< 0.1%
213541
< 0.1%
205321
< 0.1%
203571
< 0.1%
202961
< 0.1%
200551
< 0.1%
198531
< 0.1%
197871
< 0.1%
196731
< 0.1%
195891
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3023
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.080507
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:37:59.415657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile216.49
Q1552.78
median1067.485
Q32281.3525
95-th percentile7237.1505
Maximum279138.02
Range279131.82
Interquartile range (IQR)1728.5725

Descriptive statistics

Standard deviation10576.89307
Coefficient of variation (CV)3.847429365
Kurtosis346.9449453
Mean2749.080507
Median Absolute Deviation (MAD)668.135
Skewness16.53361436
Sum8357204.74
Variance111870667
MonotonicityNot monotonic
2022-09-27T08:38:00.239396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.442
 
0.1%
734.942
 
0.1%
1314.452
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
533.332
 
0.1%
3312
 
0.1%
2092.322
 
0.1%
1078.962
 
0.1%
Other values (3013)3020
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
17.111
< 0.1%
25.51
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
77183.61
< 0.1%
72882.091
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct310
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.91809211
Minimum0
Maximum372
Zeros90
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:00.789787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median32
Q385
95-th percentile252.05
Maximum372
Range372
Interquartile range (IQR)74

Descriptive statistics

Standard deviation79.95689157
Coefficient of variation (CV)1.212973389
Kurtosis2.43915936
Mean65.91809211
Median Absolute Deviation (MAD)26
Skewness1.736986768
Sum200391
Variance6393.10451
MonotonicityNot monotonic
2022-09-27T08:38:01.328249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
399
 
3.3%
090
 
3.0%
284
 
2.8%
182
 
2.7%
772
 
2.4%
967
 
2.2%
864
 
2.1%
1060
 
2.0%
460
 
2.0%
1559
 
1.9%
Other values (300)2303
75.8%
ValueCountFrequency (%)
090
3.0%
182
2.7%
284
2.8%
399
3.3%
460
2.0%
520
 
0.7%
638
 
1.2%
772
2.4%
864
2.1%
967
2.2%
ValueCountFrequency (%)
3723
0.1%
3714
0.1%
3691
 
< 0.1%
3682
 
0.1%
3661
 
< 0.1%
3655
0.2%
3631
 
< 0.1%
3592
 
0.1%
3574
0.1%
3541
 
< 0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.628947368
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:01.911705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile16
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.774185353
Coefficient of variation (CV)1.558761306
Kurtosis193.9707197
Mean5.628947368
Median Absolute Deviation (MAD)1
Skewness10.84372704
Sum17112
Variance76.98632861
MonotonicityNot monotonic
2022-09-27T08:38:02.433261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2823
27.1%
3503
16.5%
4394
13.0%
5237
 
7.8%
1218
 
7.2%
6173
 
5.7%
7138
 
4.5%
898
 
3.2%
969
 
2.3%
1055
 
1.8%
Other values (46)332
10.9%
ValueCountFrequency (%)
1218
 
7.2%
2823
27.1%
3503
16.5%
4394
13.0%
5237
 
7.8%
6173
 
5.7%
7138
 
4.5%
898
 
3.2%
969
 
2.3%
1055
 
1.8%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1691
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.121382
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:02.985284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile94
Q1288
median616
Q31390.25
95-th percentile4405.2
Maximum196844
Range196843
Interquartile range (IQR)1102.25

Descriptive statistics

Standard deviation5969.833531
Coefficient of variation (CV)3.716925507
Kurtosis437.5386043
Mean1606.121382
Median Absolute Deviation (MAD)411
Skewness17.32370424
Sum4882609
Variance35638912.39
MonotonicityNot monotonic
2022-09-27T08:38:03.599697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
1509
 
0.3%
889
 
0.3%
2728
 
0.3%
848
 
0.3%
2608
 
0.3%
2888
 
0.3%
2468
 
0.3%
5167
 
0.2%
2007
 
0.2%
Other values (1681)2957
97.3%
ValueCountFrequency (%)
11
 
< 0.1%
23
0.1%
122
0.1%
141
 
< 0.1%
161
 
< 0.1%
171
 
< 0.1%
181
 
< 0.1%
191
 
< 0.1%
201
 
< 0.1%
231
 
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
742151
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.2904605
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:04.388249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median66
Q3134.25
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)105.25

Descriptive statistics

Standard deviation267.0626546
Coefficient of variation (CV)2.201843851
Kurtosis361.8964601
Mean121.2904605
Median Absolute Deviation (MAD)44
Skewness15.84971849
Sum368723
Variance71322.46151
MonotonicityNot monotonic
2022-09-27T08:38:05.095334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2845
 
1.5%
2037
 
1.2%
3536
 
1.2%
2935
 
1.2%
1934
 
1.1%
1534
 
1.1%
1132
 
1.1%
2632
 
1.1%
2531
 
1.0%
2731
 
1.0%
Other values (459)2693
88.6%
ValueCountFrequency (%)
18
 
0.3%
214
0.5%
317
0.6%
418
0.6%
526
0.9%
630
1.0%
719
0.6%
819
0.6%
929
1.0%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3036
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.22851766
Minimum1.901111111
Maximum77183.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:05.692487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.901111111
5-th percentile4.773098383
Q112.61353223
median17.86590276
Q324.81791667
95-th percentile90.52168832
Maximum77183.6
Range77181.69889
Interquartile range (IQR)12.20438443

Descriptive statistics

Standard deviation1750.805944
Coefficient of variation (CV)21.5540797
Kurtosis1585.124574
Mean81.22851766
Median Absolute Deviation (MAD)6.152278326
Skewness39.09010284
Sum246934.6937
Variance3065321.453
MonotonicityNot monotonic
2022-09-27T08:38:06.258399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.52
 
0.1%
152
 
0.1%
14.478333332
 
0.1%
4.1622
 
0.1%
18.152222221
 
< 0.1%
30.598378381
 
< 0.1%
15.520681821
 
< 0.1%
15.486571431
 
< 0.1%
16.0351
 
< 0.1%
4.3973469391
 
< 0.1%
Other values (3026)3026
99.5%
ValueCountFrequency (%)
1.9011111111
< 0.1%
2.1505882351
< 0.1%
2.2411
< 0.1%
2.2643751
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
ValueCountFrequency (%)
77183.61
< 0.1%
56157.51
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1333
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.69481775
Minimum0
Maximum365
Zeros81
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:06.848495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q121.49166667
median41.6
Q376.54166667
95-th percentile188
Maximum365
Range365
Interquartile range (IQR)55.05

Descriptive statistics

Standard deviation61.57814682
Coefficient of variation (CV)1.014553616
Kurtosis5.750706952
Mean60.69481775
Median Absolute Deviation (MAD)24.6
Skewness2.198592687
Sum184512.246
Variance3791.868166
MonotonicityNot monotonic
2022-09-27T08:38:07.394214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
2.7%
428
 
0.9%
1021
 
0.7%
3821
 
0.7%
2721
 
0.7%
1320
 
0.7%
3120
 
0.7%
719
 
0.6%
2118
 
0.6%
1716
 
0.5%
Other values (1323)2775
91.3%
ValueCountFrequency (%)
081
2.7%
0.41
 
< 0.1%
0.53
 
0.1%
0.751
 
< 0.1%
112
 
0.4%
1.1611570251
 
< 0.1%
1.3778801841
 
< 0.1%
1.51
 
< 0.1%
1.8383233531
 
< 0.1%
215
 
0.5%
ValueCountFrequency (%)
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3611
 
< 0.1%
3563
0.1%
3542
0.1%
3512
0.1%
3502
0.1%
3493
0.1%
3481
 
< 0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1234
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1592653739
Minimum0.005464480874
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:08.039884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005464480874
5-th percentile0.008968609865
Q10.01657458564
median0.02683278048
Q30.05418524712
95-th percentile1
Maximum17
Range16.99453552
Interquartile range (IQR)0.03761066148

Descriptive statistics

Standard deviation0.508219863
Coefficient of variation (CV)3.19102546
Kurtosis407.7223613
Mean0.1592653739
Median Absolute Deviation (MAD)0.01311533354
Skewness14.25886203
Sum484.1667366
Variance0.2582874291
MonotonicityNot monotonic
2022-09-27T08:38:08.668357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1210
 
6.9%
261
 
2.0%
0.0238095238117
 
0.6%
0.062516
 
0.5%
0.0158730158715
 
0.5%
0.0294117647114
 
0.5%
0.0192307692314
 
0.5%
0.0454545454514
 
0.5%
0.0714285714313
 
0.4%
0.0181818181813
 
0.4%
Other values (1224)2653
87.3%
ValueCountFrequency (%)
0.0054644808741
 
< 0.1%
0.0054794520552
0.1%
0.0054945054951
 
< 0.1%
0.0056022408963
0.1%
0.0056338028172
0.1%
0.0056818181821
 
< 0.1%
0.0056980056983
0.1%
0.0057142857144
0.1%
0.0057306590261
 
< 0.1%
0.0057471264371
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
61
 
< 0.1%
42
 
0.1%
34
 
0.1%
261
 
2.0%
1.51
 
< 0.1%
1.3333333331
 
< 0.1%
1210
6.9%
0.66666666673
 
0.1%
0.55495978551
 
< 0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct215
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.21973684
Minimum0
Maximum80995
Zeros1535
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:09.624267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile98.1
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation2010.747881
Coefficient of variation (CV)23.59486143
Kurtosis1470.661412
Mean85.21973684
Median Absolute Deviation (MAD)0
Skewness38.01023083
Sum259068
Variance4043107.043
MonotonicityNot monotonic
2022-09-27T08:38:10.184747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01535
50.5%
1169
 
5.6%
2150
 
4.9%
3105
 
3.5%
489
 
2.9%
678
 
2.6%
561
 
2.0%
1252
 
1.7%
744
 
1.4%
843
 
1.4%
Other values (205)714
23.5%
ValueCountFrequency (%)
01535
50.5%
1169
 
5.6%
2150
 
4.9%
3105
 
3.5%
489
 
2.9%
561
 
2.0%
678
 
2.6%
744
 
1.4%
843
 
1.4%
941
 
1.3%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2002
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.5867541
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:10.772812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43.31666667
Q1102.15
median172
Q3281.0833333
95-th percentile604.85
Maximum74215
Range74214
Interquartile range (IQR)178.9333333

Descriptive statistics

Standard deviation1556.994657
Coefficient of variation (CV)5.649744171
Kurtosis1820.819008
Mean275.5867541
Median Absolute Deviation (MAD)83.5
Skewness40.99969797
Sum837783.7323
Variance2424232.363
MonotonicityNot monotonic
2022-09-27T08:38:11.318172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
8610
 
0.3%
829
 
0.3%
739
 
0.3%
888
 
0.3%
758
 
0.3%
608
 
0.3%
1368
 
0.3%
1637
 
0.2%
Other values (1992)2952
97.1%
ValueCountFrequency (%)
12
0.1%
22
0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct920
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.11154116
Minimum0.2
Maximum415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2022-09-27T08:38:11.907768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.75
median13.66666667
Q322.55
95-th percentile47.66904762
Maximum415
Range414.8
Interquartile range (IQR)14.8

Descriptive statistics

Standard deviation18.42998867
Coefficient of variation (CV)1.017582574
Kurtosis90.58593197
Mean18.11154116
Median Absolute Deviation (MAD)6.821428571
Skewness6.209684015
Sum55059.08512
Variance339.6644822
MonotonicityNot monotonic
2022-09-27T08:38:12.434432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
944
 
1.4%
1343
 
1.4%
840
 
1.3%
1439
 
1.3%
1639
 
1.3%
1738
 
1.2%
1138
 
1.2%
736
 
1.2%
536
 
1.2%
1535
 
1.2%
Other values (910)2652
87.2%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
4151
< 0.1%
2591
< 0.1%
1811
< 0.1%
1771
< 0.1%
1711
< 0.1%
1601
< 0.1%
1481
< 0.1%
1431
< 0.1%
1411
< 0.1%
1381
< 0.1%

Interactions

2022-09-27T08:37:46.043244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:01.400987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:08.557545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:17.076907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:24.409995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:34.090983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:43.962114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:52.447543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:01.435566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:10.231831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:19.948485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:27.947322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:36.681233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:46.642543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:02.222451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:09.033888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:17.598110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:25.206687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:34.702421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:44.640620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:53.015625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:02.048002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:11.376642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:20.562920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:28.466437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:37.524825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:47.351050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:02.781847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:09.552278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:18.492700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:25.954217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:35.800193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:45.283056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:53.973279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:02.652402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:12.065130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:21.397512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:28.982418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:38.180289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:48.062585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:03.265008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:10.079624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:19.093150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:26.892883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:36.297546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:46.087622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:54.622360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:03.433152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:12.808664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:22.076169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:29.513147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:38.849763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:49.090922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:03.798360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:10.800134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:19.580497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:27.669434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:36.810910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:46.831153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:55.298709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:03.994570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:13.536334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:22.707636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:30.266687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:39.659336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:49.827420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:04.409822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:11.759816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:20.021781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:28.217828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:37.409335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:47.389543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:55.781050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:05.019269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:14.201780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:23.156086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:30.782073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:40.412898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:50.584960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:04.973192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:12.809557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:20.525138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:28.866282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:37.956729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:48.239149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:56.580624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:05.676849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:15.078402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:23.715075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:31.584615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:41.404587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:51.118359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:05.564614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:13.555603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:21.029633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:29.560779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:38.580300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:48.697469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:57.262804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:06.391330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:15.693838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:24.394118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:32.192052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:42.264189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:51.800816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:06.103993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:14.151019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:21.535079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:30.092150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:39.335836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:49.241265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:58.003334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:07.180889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:16.495214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:24.904506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:32.951583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:42.821244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:52.331100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:06.601150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:14.779471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:22.020423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:30.783639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:40.262494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:50.035855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:58.771878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:07.769092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:17.044436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:25.449866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:33.696111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:43.386623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:52.819425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:07.106898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:15.434748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:22.544918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:31.494143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:41.166133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:50.673286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:59.531414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:08.361510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:17.757960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:25.940480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:34.577738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:43.938754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:53.309246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:07.615258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:16.093215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:23.114346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:32.648962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:42.323955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:51.283718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:00.273963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:09.035983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:18.632553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:26.451355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:35.321266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:44.593216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:53.835335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:08.109615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:16.589321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:23.732785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:33.515575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:43.162553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:36:51.875131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:00.852352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:09.714470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:19.401097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:27.084366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:36.060814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-27T08:37:45.527886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-27T08:38:12.920207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-27T08:38:13.700968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-27T08:38:14.542538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-27T08:38:15.413182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-27T08:37:54.568068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-27T08:37:55.857715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.2137134173329718.1522222.09090917.00000040.050.9705880.617647
11130473232.59569139017118.90403522.9285710.02830235.0154.44444411.666667
22125836705.38215502823228.90250022.7500000.04043150.0335.2000007.600000
3313748948.259554392833.86607192.3333330.0179210.087.8000004.800000
4415100876.003333803292.0000008.2000000.07317122.026.6666670.333333
55152914623.302514210210245.32647118.8888890.04011529.0150.1428574.357143
66146885630.87721362132717.21978613.7307690.057221399.0172.4285717.047619
77178095411.91151220576188.71983627.1538460.03352041.0171.4166673.833333
881531160767.9009138194237925.5434642.7350430.243316474.0419.7142866.230769
99160982005.638776136729.93477640.2857140.0278750.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
3030556817852114.3411253244.7641670.02.0000000.026.50000010.500000
3031557012479473.201013823015.7733333.51.00000034.0382.00000030.000000
3032558517772182.7710258533.4484910.02.0000000.029.00000025.000000
3033559114126706.13735081547.0753331.01.00000050.0169.3333334.666667
3034559216479300.8392102358.5951430.02.0000000.051.00000017.500000
30355597135211092.39037334352.5112413.50.3333330.0244.333333104.000000
3036560715060301.84742621202.5153330.04.0000000.065.50000020.000000
3037562412558269.96711961124.5418185.01.000000196.0196.00000011.000000
303856751600012393.7023511091377.0777780.03.0000000.01703.3333333.000000
3039567714087194.4221251692.8176810.01.0000001.0251.00000061.000000